Computerized maintenance management systems for vehicles such as cars and trucks are used to gather data relating to a specific fleet of vehicles and predict faults and failures in vehicle components utilizing well known predictive modeling algorithms. Diagnostic data through vehicle on-board telematics computers and diagnostics data collected through hardware readers located at a repair or maintenance facilities and/or other sources, combined with vehicle repair data captured by a technician at a technician console or via manual data entry at the repair shop, can be correlated to determine component aging, status, faults and failures. This combination of data sets are used to provide predictions regarding failure rates and failure timing of vehicle components, which can significantly reduce costs and improve safety by allowing for more focused preventative maintenance of the vehicles, thereby avoiding the high cost and environmental impact of vehicle breakdown in the heavy duty industry.
Examples of such systems are found in U.S. Pat. Nos. 4,943,919, 5,764,509, 6,301,531, 6,434,512, 6,553,290, 6,859,739, 7,065,433, 7,096,074, 7,230,527, 7,233,886, 7,308,385, 7,373,283, 7,539,597, 7,630,802, 7,636,648, 7,640,145, 7,689,394, 7,882,394, 7,945,427, 8,095,261, 8,165,826, 8,346,429, 8,356,207, 8,396,622, 8,423,226, 8,429,467 and 8,442,702; all of which are incorporated herein by reference in their entireties; and published US patent application nos. 20010033225, 20020184178, 20030139908, 20030191564, 20040078171, 20040158367, 20040243636, 20050096873, 20060229777, 20070198215, 20070250229, 20090216393, 20100042287, 20100262431, 20120316832 and 20130079972; all of which are incorporated herein by reference in their entireties.
However, in these prior art systems the data is stored in a data repository or ‘warehouse’, and must be extracted from the data warehouse and fed into the modeling software for processing, as shown in FIG. 1. In a typical implementation the data is communicated from the fleet maintenance facility to the data warehouse (via a communications link if necessary), then a fleet manager must extract data from a CMMS, in a compatible format, and import that CMMS data into stochastic software in order to create a predictive model. Since this is a resource-intensive and lengthy process, it is not uncommon that by the time the CMMS data is extracted from the CMMS and imported to the stochastic software, the relevant data set in the CMMS itself has already changed due to the capture of new repair data. This means that the data is never ‘fresh’ when it is processed by the modeling software, and commensurately, predictive models created under this methodology will always be “stale”. Working with aged data can have a material adverse effect on the accuracy of fault and failure predictions.
Also, the population of vehicles from which data can be extracted for modeling is often limited to a particular repair or maintenance facility, which limits the precision that can be obtained from the modeling algorithms. Currently, there is no way for one fleet to compare its modeled data with that of another fleet in order to determine whether, on a relative basis, they are cost-optimized in managing unexpected failures. Due to the complexity of extracting CMMS data and importing that data into stochastic software, most fleets cannot even produce predictive cost optimization models, let alone share their modeled data with other fleets in order to determine peer performance.
Particularly in some industries, such as the heavy truck industry where trucks are often designed for specific purposes, it can be difficult to temporarily replace a vehicle that has experienced an unexpected fault or failure. When passenger vehicles unexpectedly fail, replacement vehicles can usually be found due to the ubiquity of national rental fleets. When heavy-duty vehicles unexpectedly fail, it is often extremely difficult to find like-duty replacements. In addition to lost revenue, and potentially delivery penalties and lost driver hours (due to non-revenue generating activities), an unexpected failure can also result in additional costs for transporting maintenance crews, equipment, overnight hotel stays and an overall increase in CO2 emissions.
Accordingly, the accuracy and currency of fault and failure predictions is much more than a convenience. It can be the difference between a safe, reliable vehicle and costly (and in extreme cases hazardous) consequences.